Assessment of environmental factors influencing SARS-CoV-2 in Vietnam's surface water across two years of clinical data.

Wastewater-based epidemiology (WBE) is an effective, non-invasive method for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by tracking viral prevalence in water. This study aimed to investigate the presence of SARS-CoV-2 in surface water in Vietnam over two years. One-step quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to quantify SARS-CoV-2 and its variant-specific mutation sites (G339D/E484A) and pepper mild mottle virus (PMMoV) from a total of 315 samples (105 samples per site) to compare with reported Coronavirus disease 2019 (COVID-19) cases and environmental factors.

Low antiviral uptake of nirmatrelvir/ritonavir and molnupiravir in adult patients with COVID-19 in Taiwan in 2022.

Background: Antivirals are effective in reducing hospitalisation and death in mild-to-moderate coronavirus 2019 (COVID-19) patients. We estimated the antiviral uptake of nirmatrelvir/ritonavir and molnupiravir in adult patients with a syndrome coronavirus 2 (SARS-CoV-2) infection during the Emergency Use Authorization (EUA) period in Taiwan.

Methods: A retrospective cohort study was conducted in Taiwan between January 2022 and December 2022.

Analysis of structural defects and their influence on red-emitting γ-AlO:Mn,Mg nanowires using positron annihilation spectroscopy.

The present paper reported on the analysis of structural defects and their influence on the red-emitting γ-AlO:Mn,Mg nanowires using positron annihilation spectroscopy (PAS). The nanowires were synthesized by hydrothermal method and low-temperature post-treatment using glucose as a reducing agent. X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence (PL), and photoluminescence excitation (PLE) were utilized, respectively, for determining the structural phase, morphology and red-emitting intensity in studied samples.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.